Kim Byungwhan, Lee Joogong, Jang Jungyoung, Han Dongil, Kim Ki-Hyun
Department of Electronic Engineering, Sejong University, Seoul, Korea.
ScientificWorldJournal. 2011 May 5;11:992-1004. doi: 10.1100/tsw.2011.95.
Models to predict seasonal hydrogen sulfide (H2S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H2S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H2S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H2S. It was noted that H2S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H2S can be accounted for by a diverse combination of meteorological parameters across seasons.
利用神经网络构建了预测季节性硫化氢（H₂S）浓度的模型。为此，考虑并优化了两种广义回归神经网络和径向基函数网络。H₂S的输入数据于2005年8月至2006年秋季从韩国安山市的一个大型工业园区收集。准备了三种季节性分组，并为每个数据集构建了一个优化模型。然后将这些优化模型用于分析参数的敏感性和主要影响。结果表明，春季和夏季H₂S对降雨非常敏感。秋季，其敏感性对风速和气压有很强的依赖性。春季和夏季，气压被确定为最具影响力的参数。秋季，相对湿度对H₂S的影响最为显著。研究发现，H₂S与许多参数（如辐射、风速或露点温度）呈反比关系。相反，随着气压降低，H₂S呈下降趋势。春季和夏季辐射增加可能会减少，但秋季预测会出现相反的趋势。因此，本研究的总体结果表明，不同季节气象参数的多种组合可以解释H₂S的行为。
